{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JvygCSGWAfKs"
   },
   "source": [
    "# 载入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6i7maQ8GAze3"
   },
   "outputs": [],
   "source": [
    "data_dir = {\n",
    "    'train_data': '/content/data/mchar_train/',\n",
    "    'val_data': '/content/data/mchar_val/',\n",
    "    'test_data': '/content/data/mchar_test_a/',\n",
    "    'train_label': '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/mchar_train.json',\n",
    "    'val_label': '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/mchar_val.json',\n",
    "    'submit_file': '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/mchar_sample_submit_A.csv'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ziiRRPnlCh9R"
   },
   "source": [
    "# 导入相关库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3_P18kxkChaF"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from glob import glob\n",
    "import torch as t\n",
    "t.random.manual_seed(0)\n",
    "t.cuda.manual_seed_all(0)\n",
    "t.backends.cudnn.benchmark = True\n",
    "t.backends.cudnn.deterministic = True\n",
    "from PIL import Image\n",
    "import torch.nn as nn\n",
    "from tqdm.auto import tqdm\n",
    "from torchvision import transforms\n",
    "from torchvision.utils import save_image, make_grid\n",
    "from torch.optim import SGD, Adam\n",
    "from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts, MultiStepLR, CosineAnnealingLR\n",
    "from torch.utils.data import DataLoader, Dataset, WeightedRandomSampler\n",
    "import pandas as pd\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patch \n",
    "import torch.nn.functional as F\n",
    "import json\n",
    "from torchvision.models.mobilenet import mobilenet_v2\n",
    "from torchvision.models.resnet import resnet18, resnet34\n",
    "from torchsummary import summary\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8_spj-S6AmmT"
   },
   "source": [
    "# 查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "OFbUNRpX9q5G",
    "outputId": "589d32c0-6b04-47ce-a6b6-f5fb293fe04e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train image counts: 30000\n",
      "val image counts: 10000\n",
      "test image counts: 40000\n"
     ]
    }
   ],
   "source": [
    "def data_summary():\n",
    "  train_list = glob(data_dir['train_data']+'*.png')\n",
    "  test_list = glob(data_dir['test_data']+'*.png')\n",
    "  val_list = glob(data_dir['val_data']+'*.png')\n",
    "  print('train image counts: %d'%len(train_list))\n",
    "  print('val image counts: %d'%len(val_list))\n",
    "  print('test image counts: %d'%len(test_list))\n",
    "\n",
    "data_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_VMYXTkpGy0D",
    "outputId": "db553cc2-acd3-4e94-c952-92428d9f1c42"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'height': [219, 219], 'label': [1, 9], 'left': [246, 323], 'top': [77, 81], 'width': [81, 96]}\n"
     ]
    }
   ],
   "source": [
    "def look_train_json():\n",
    "  with open(data_dir['train_label'], 'r', encoding='utf-8') as f:\n",
    "    content = f.read()\n",
    "\n",
    "  content = json.loads(content)\n",
    "\n",
    "  print(content['000000.png'])\n",
    "\n",
    "look_train_json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "colab_type": "code",
    "id": "Alt5F_c9H3pi",
    "outputId": "d479b615-6642-4b97-9278-a85a12092a66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    file_name  file_code\n",
      "0  000000.png          0\n",
      "1  000001.png          0\n",
      "2  000002.png          0\n",
      "3  000003.png          0\n",
      "4  000004.png          0\n"
     ]
    }
   ],
   "source": [
    "def look_submit():\n",
    "  df = pd.read_csv(data_dir['submit_file'], sep=',')\n",
    "  print(df.head(5))\n",
    "\n",
    "look_submit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "colab_type": "code",
    "id": "kDDFZoCWIjh_",
    "outputId": "87f66e9d-f382-40bf-975d-83747e9bc019"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_samples():\n",
    "  imgs = glob(data_dir['train_data']+'*.png')\n",
    "  fig, ax = plt.subplots(figsize=(12, 8), ncols=2, nrows=2)\n",
    "  marks = json.loads(open(data_dir['train_label'], 'r').read())\n",
    "\n",
    "\n",
    "  for i in range(4):\n",
    "    \n",
    "    img_name = os.path.split(imgs[i])[-1]\n",
    "    mark = marks[img_name]\n",
    "\n",
    "\n",
    "    img = Image.open(imgs[i])\n",
    "    img = np.array(img)\n",
    "\n",
    "    bboxes = np.array(\n",
    "        [mark['left'],\n",
    "        mark['top'],\n",
    "        mark['width'],\n",
    "        mark['height']]\n",
    "    )\n",
    "    ax[i//2, i%2].imshow(img)\n",
    "    for j in range(len(mark['label'])):\n",
    "      rect = patch.Rectangle(bboxes[:, j][:2], bboxes[:, j][2], bboxes[:, j][3], facecolor='none', edgecolor='r')\n",
    "      ax[i//2, i%2].text(bboxes[:, j][0], bboxes[:, j][1], mark['label'][j])\n",
    "      ax[i//2, i%2].add_patch(rect)\n",
    "  plt.show()\n",
    "\n",
    "plot_samples()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 546
    },
    "colab_type": "code",
    "id": "h_Yl-adlooZQ",
    "outputId": "0d5c644a-d3ac-451a-ab2b-7e6f1c34e830"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:  [128.27233333  57.23066667]\n",
      "median:  [104.  47.]\n"
     ]
    }
   ],
   "source": [
    "def img_size_summary():\n",
    "  sizes = []\n",
    "\n",
    "  for img in glob(data_dir['train_data']+'*.png'):\n",
    "    img = Image.open(img)\n",
    "\n",
    "    sizes.append(img.size)\n",
    "\n",
    "  sizes = np.array(sizes)\n",
    "\n",
    "  plt.figure(figsize=(10, 8))\n",
    "  plt.scatter(sizes[:, 0], sizes[:, 1])\n",
    "  plt.xlabel('Width')\n",
    "  plt.ylabel('Height')\n",
    "\n",
    "  plt.title('image width-height summary')\n",
    "  plt.show()\n",
    "\n",
    "  return np.mean(sizes, axis=0), np.median(sizes, axis=0)\n",
    "\n",
    "mean, median = img_size_summary()\n",
    "print('mean: ', mean)\n",
    "print('median: ', median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "colab_type": "code",
    "id": "t0ypIGBjrCRl",
    "outputId": "77802c5c-933e-489f-ab94-89ebf9c339bf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3nSmp7n1dl7RSkrzbT5P0g16MDwAAAMiiyGoey81s3Pu6JumXJX1HraD6nd5u75f0Fe/rO7zv5d2+xznnihofAAAA0K0lnXfJ7AxJnzWzilpB+63Ouf9mZt+W9CUzu1HSfkmf9vb/tKS/MLOHJR2V9J4CxwYAAAB0rbBg2jn3LUnrQ7b/s1r50+3bn5f0rqLGAwAAijG9v64duw/pqdmGVozXNLVxjTatn+h8xwE3qs8bCxU5Mw0AAIbc9P66tu46qEZzTpJUn21o666DkjTUgeWoPm8sRjtxAACQ2Y7dh+YDSl+jOacduw/1aUS9MarPG4sRTAMAgMyemm2k2j4sRvV5YzGCaQAAkNmK8Vqq7cNiVJ83FiOYBgAAmU1tXKNatbJgW61a0dTGNX0aUW+M6vPGYixABAAAmfmL7UatqsWoPm8sZoPcF2VyctLNzMz0exgAAAAYcma2zzk32b6dNA8AAAAgI4JpAAAAICOCaQAAACAjgmkAAAAgI4JpAAAAICOCaQAAACAjgmkAAAAgI4JpAAAAICOCaQAAACAjgmkAAAAgI4JpAAAAICOCaQAAACAjgmkAAAAgI4JpAAAAICOCaQAAACAjgmkAAAAgI4JpAAAAICOCaQAAACAjgmkAAAAgI4JpAAAAICOCaQAAACAjgmkAAAAgI4JpAAAAICOCaQAAACAjgmkAAAAgI4JpAAAAICOCaQAAACAjgmkAAAAgI4JpAAAAIKMl/R4AACCb6f117dh9SE/NNrRivKapjWu0af3EwBwfAIYBwTQADKDp/XVt3XVQjeacJKk+29DWXQclKZeAt+jjA8CwIM0DAAbQjt2H5gNdX6M5px27Dw3E8QFgWBBMA8AAemq2kWp72Y4PAMOCYBoABtCK8Vqq7WU7PgAMC4JpABhAUxvXqFatLNhWq1Y0tXHNQBwfAIYFCxABYAD5iwCLqrZR9PEBYFiYc67fY8hscnLSzczM9HsYAAAAGHJmts85N9m+nTQPAAAAICOCaQAAACAjgmkAAAAgo8KCaTNbaWZ3m9m3zexBM/s/vO3Xm1ndzA54/94WuM9WM3vYzA6Z2caixgYAAADkochqHi9J+pBz7n4ze6WkfWb2de+2m5xz/3dwZzP7WUnvkXSepBWS/oeZ/bRzbmELLgAAAKAkCpuZds497Zy73/v6R5K+IymuptI7JH3JOfeCc+5RSQ9LelNR4wMAAAC61ZOcaTNbLWm9pHu9Tb9jZt8ysz8zs2XetglJhwN3e1LxwTcAAADQV4UH02b2Ckm3S7raOfdDSZ+SdI6kdZKelvTxlMf7gJnNmNnMkSNHch8vAAAAkFShwbSZVdUKpL/gnNslSc657znn5pxzJyT9qU6mctQlrQzc/Uxv2wLOuZudc5POucnly5cXOXwAAAAgVpHVPEzSpyV9xzn3icD2MwK7/Zqkf/S+vkPSe8zsFDM7W9JrJd1X1PgAAACAbhVZzWODpF+XdNDMDnjbfl/SlWa2TpKT9Jik35Ik59yDZnarpG+rVQnkg1TyAFC06f117dh9SE/NNrRivKapjWu0aX2xyzX68ZgAgGKYc67fY8hscnLSzczM9HsYAAbU9P66tu46qEbz5Of2WrWibZevLSy47cdjAgC6Z2b7nHOT7dvpgAhgZO3YfWhBUCtJjeacduw+NFSPCQAoDsE0gJH11Gwj1fZBfUwAQHEIpgGMrBXjtVTbB/UxAQDFIZgGMLKmNq5RrVpZsK1WrWhq45qhekwAQHGKrOYBAKXmL/jrZWWNfjwmAKA4VPMAAAAAOoiq5sHMNAAMKOpVDzbeP2A4EEwDwABqr1ddn21o666DkkRANgB4/4DhwQJEABhA1KsebLx/wPAgmAaAAUS96sHG+wcMD4JpABhA1KsebLx/wPAgmAaAAUS96sHG+wcMDxYgAsAAol71YOP9A4YHdaYBAACADqLqTJPmAQAAAGREMA0AAABkRDANAAAAZEQwDQAAAGREMA0AAABkRDANAAAAZESdaQA9Nb2/Tm3dkuE9AYDsCKYB9Mz0/rq27jqoRnNOklSfbWjrroOSRPDWJ7wnANAd0jwA9MyO3YfmgzZfozmnHbsP9WlE4D0BgO4QTAPomadmG6m2o3i8JwDQHYJpAD2zYryWajuKx3sCAN0hmAbQM1Mb16hWrSzYVqtWNLVxTZ9GBN4TAOgOCxAB9Iy/oI3KEeXBewIA3THnXL/HkNnk5KSbmZnp9zAAlNSglXwbtPECwCgxs33Oucn27cxMA8hVWQLCPEu+9eI5UaIOAAYTOdMAcuMHhPXZhpxOBoTT++s9H0teJd969ZwoUQcAg4lgGkBuyhQQ5lXyrVfPiRJ1ADCYCKYB5KZMAWFeJd969ZwoUQcAg4lgGkBuyhQQ5lXyrVfPiRJ1ADCYCKYB5KZMAeGm9RPadvlaTYzXZJImxmvadvna1Iv5evWc8hovAKC3KI0HIFdlqeaRp2F8TgCAdKJK4xFMAwAAAB1QZxoAMBS4UgD0Bj9ryRBMAwAGBs1tgN7gZy05FiACAAZGmWqZA8OMn7XkmJkGAAyMMtUyx+AifaEzftaSY2YaADAwylTLHIPJT1+ozzbkdDJ9YXp/vd9DKxV+1pIjmAYADIwy1TLHYCJ9IRl+1pIjzQMAMDD8S/FcokdWpC8kw89acgTTAICBsmn9BH/QkdmK8ZrqIYEz6QuL8bOWDGkeAABgZJC+gLwxMw0AAEYG6QvIG8E0AAAYKaQvIE+FBdNmtlLS5yS9WpKTdLNz7g/N7HRJOyWtlvSYpHc7546ZmUn6Q0lvk3Rc0r91zt1f1PgA9A41XdGtXp5DnK8A0igyZ/olSR9yzv2spIskfdDMflbSFknfcM69VtI3vO8l6Vckvdb79wFJnypwbAB6hJqu6FYvzyHOVwBpFRZMO+ee9meWnXM/kvQdSROS3iHps95un5W0yfv6HZI+51rukTRuZmcUNT5g2E3vr2vD9j06e8ud2rB9T9+CAWq6jqY8z79enkOcrwDS6knOtJmtlrRe0r2SXu2ce9q76btqpYFIrUD7cOBuT3rbnhaAVPzZNT8o8GfXJPX8cjU1XUdP3udfL88hzlcAaRVeGs/MXiHpdklXO+d+GLzNOefUyqdOc7wPmNmMmc0cOXIkx5ECw6NMs2u0pB09eZ9/vTyHOF8BpFVoMG1mVbUC6S8453Z5m7/np294/z/jba9LWhm4+5netgWcczc75yadc5PLly8vbvDAACvT7Bo1XUdP3udfL88hzlcAaRUWTHvVOT4t6TvOuU8EbrpD0vu9r98v6SuB7e+zloskPRtIBwGQQplm1zatn9C2y9dqYrwmkzQxXtO2y9dSHWGI5X3+9fIc4nwFkJa1Mi0KOLDZz0v6O0kHJZ3wNv++WnnTt0paJelxtUrjHfWC7z+R9Fa1SuP9hnNuJu4xJicn3cxM7C7ASGrPWZVas2sEBegFzj8Aw8jM9jnnJhdtTxJMm9lfOOd+vdO2XiOYBqKVtVZuWcc1yop4T4LHHF9alXPSs43mSL3nnOvAcOk2mL7fOfeGwPcVSQe9GtJ9QzANDBZmLMun6PdkVN/zUX3ewDCLCqZjc6bNbKuZ/UjS683sh96/H6m1aPArcfcFgHZlqjKClqLfk1F9z0f1eQOjKDaYds5tc869UtIO59y/8P690jn3E865rT0aI4AhUaYqI2gp+j0Z1fd8VJ83MIoSVfNwzm01swkz+1dm9q/9f0UPDsBwKVOVEbQU/Z6M6ns+qs8bGEWJgmkz2y5pr6RrJU15//5DgeMCMISo4Vs+Rb8no/qej+rzBkZR0nbivyZpjXPuhSIHA2C4+QuvqHBQHkW/J6P6no/q8wZGUdJqHl+T9C7n3I+LH1JyVPMAAABAL0RV84idmTazP5bk1GqicsDMviFpfnbaOffv8x4oAPRLv+sC9/vxAQDpdUrz8Kd996nV7hsAhlJ7XeD6bENbdx2UpJ4EtP1+fABANrHBtHPus70aCAD0U1xd4F4Es/1+fABANokWIJrZQbXSPYKeVWvm+kbn3A/yHhgA9FK/6wL3+/EBANkkrebxNUlzkr7off8eSUslfVfSZyRdlvvIAKCHVozXVA8JXHtVF7jfjw8AyCZRnWlJv+Sc2+qcO+j9+7CkX3DOfUzS6uKGBwC90e+6wP1+fABANklnpitm9ibn3H2SZGZvlOT/1n+pkJEBGEhFVqQo8tj9rgvc78cHAGSTtM70GyX9maRXSDJJP5T0v0h6UNKlzrlbixxkFOpMA+XSXpFCas2ubrt8bddBYZHHBgCgk6g604nSPJxz33TOrZW0TtL5zrnXO+fuc849169AGkD5xFWkKPOxAQDIqlPTlqucc583s99t2y5Jcs59osCxARgwRVakoNoFAKCMOs1Mv9z7/5UR/wBgXlTliTwqUhR5bAAAskqUM11W5EwD5RKW12xqFamf6HJBXbc501GLF2nhnR6vGYBRFJUznXQB4k9L+pSkVzvnXmdmr5f0dufcjfkPNTmCaaB8/ECrPtuYD6SDli2t6rrLzssUfGUN4qIC8SsumNDt++osakyBhaAARlW3wfTfSJqS9F+dc+u9bf/onHtd7iNNgWAa6E6RM4wbtu8JbUIi5R98dXoeUWOpmGku5HfgxHhNe7dcksvY+qEf7+ugv2YA0ElUMJ20zvRS59x9/sJDD/WlgQHWPsNYn21o666DkpRL4BW3MNCvwpHH4yR5HlFjCQuk4/bvNI4ypD70631lISiAUZW0A+L3zewceVdszeydkp4ubFQACpdnqbnp/XVt2L5HZ2+5Uxu279H0/nrHhYH12cb8vt1I8jyixlJZOEHQcf8ofgBbn23I6WQA2+1zy6LoEoIsBAWAhZIG0x+U9F8lnWtmdUlXS/rtwkYFoHB5zTBGBZIXn7t8UXvsdnkEnUmeR1Sr7isvXJlLC+8y1cAueuaYtucAsFDSYLou6c8lfVTSlyR9XdL7ixoUgOLlNcMYFUje/dARbbt8rcZr1dj7dxt0Jnkem9ZPaNvlazUxXpOpld+77fK1unHT2tDtadMhypT6UPTMcdRryeJDAKMqac70VyTNSrpf0lPFDQdAr0xtXBNalSHtDGNcILlp/cSC8nNRCxK7CTqTPg9/LO2itqexYrwW+tz6kfqQ1/saJ4/XDACGRdJg+kzn3FsLHQmAnvKDoW4XzSUJJP3gK6oSRDdBZ17PQ8q+iLAXAWzSMeb5euQ9NgAYRklL490s6Y+dcweLH1JylMYD+i9N3eEy1yguqilMmcbY7WPHPb8yv7cAkIeo0nixOdNmdtDMviXp5yXdb2aHzOxbge0ARlyaHNoy59v2chFhWPWTso0xKEm1kjItwgSAXuqU5vGrPRkFgIGWJoe2m3zbImd/u1lEmKa2czd1oPu10DEuUO5Uy/up2QbpHwCGWmww7Zx7vFcDAYA4RTcj6WYRYZJgM+2+YQFovxY6Jgnio8Y2vrRa6PsGAP2WtDQeAPRV0WkE3dRPTjNjnGTfNLW7e1HjOUm5vajXzzmR/gFgqBFMAyMga45uGR7LP15UWb28Oil2k8+dprZzkn071e7udc55kg8aUa/fs41m6DFpPw5gWCQtjQdgQBWdHlHkY4VViAiT13PKms+dpjRekn2T1O7upaTl9sLGFlVfnPbjAIYFwTQw5NLk85btscKOF6Wo55REmtrOcfv6edJRBUv7GYD24oMGAAwigmkkwmr8wZU0nzeP9zjvahNp79fP1IFuK5p0moUvUwCa5lzppokMv3cADAKCaXTUyzQB5C9JBYi83uO8q01EHS9u/7S6CdjyCPY6tVqXWvnHZQkks5wrWWa1+b0DYFCwABEd0YyhpZeL+PKUZPFYXu9x2GOZsi8SDDtelE4zt9dOH9Q5W7+q1Vvu1Dlbv6prpw8makYSZXp/XVO3PbDgvlO3PZDqOQYfP4pJ2rvlktIEkL36fcDvHQCDgplpdNSvRhFlMsizZEkus+f1Hgcfqz7bkEnz+b+dXrO4Wd5uZ26vnT6oz9/zxPz3c87p8/c8oV37nlSjeWLBvklzr6+/40E1TyzMbm6ecLr+jgcTnxNJcsLHl1YTHSuorM1tyvg4ANAtgml01LJKW10AACAASURBVK9GEWXSy0V8Reh0mT3P99h/rLBydlGvWacPK/7ivLCFbElKw91y7+HQ7cfbAmlfkoBtNqLkW9T2sAA3yeMcO97UOVu/qisvXKkbN63tuH+Zm9uU8XEAoFukeaCjbppZDIthnyUr4j1O85oluaTfTR3oORdVHyNc3gFbVDrJabVks87+TPq10wc77lvm5jZlfBwA6BYz0+iom9X4w2JQZsmunT6oW+49rDnnVDFLPJtZxHuc5jVLGnhnLc9WMQsNqM2kU5dUMpVtW7a0qmPHF89CLwtJy4gKcE+tjqlWrXRM9fDdcu/hju9n0R/8evX7gN87AAYFwTQS6UejiDIZhFq5UXnBkhIH1Hm+x2les6I/rFz0mmXa+8jRRdv/1WtO17smV2UK2C59/RkLXu/g9nZRgezs8aZu2rwuNMc8TJIZ9l588OvV74NR/70DYDAQTAMJDMIsWVRe8C33HtbkWaf3fOxRr5kkbdi+Z8G2sMA7WAWk23J1j/0gPJh97AcLOwr6971m54EFr1PYjP/dDx0JPebdDx1ZNIbxiFnsFeO10MePWmxZMev4/Lv94EdtZwwTzmf0grmUuYRlMjk56WZmZvo9DJTcqPwyXb3lzsjb2lMJki7cy1vcIkIpvApImvFGHT8qjcIkPbr90tj7vmHVaaGz2nHaH7M6ZpJJzTm3YJ+o59R+lcF31UWrEi9CzNokJesiT6BsOJ+RNzPb55ybXLSdYBrDbJR+mcYF02Emxmvau+WS0NuK+gASVuGjfSxJ9kl7/DGTToT8qlu2tKr9H3lL7H3TisrPHq9V9fJTliR+TbPmv3ejm9ceKBvOZ+QtKpgmzQNDrdcl7QZpFjwqcCyytFpU7nAwnaObBXRR+4QF0pLkXLIOhFHCZvyjZsGfbTR14Lq3JD72jZvWhgbPZaghPUjneS/06vXgdU9n2KswoTwKK41nZn9mZs+Y2T8Gtl1vZnUzO+D9e1vgtq1m9rCZHTKzjUWNC6Oll79Mu+mm1+3jbti+J9N9/U6AQVlKqyXtDhm3CM7vIBjVpCTJArq0i+xmG82OHQjjhJXqm4gYQx4LAIs+x6LGGNZ6vtfneTeK7F4a9npcs/OAVuf8WIP4uvdbkvMZyEORdaY/I+mtIdtvcs6t8/59VZLM7GclvUfSed59/h8zS9ZDGIjRy1+m/Wh/3KkddadW3GH1i9N+AEnzR75Te/DmCafnm3OZ6wunaT8utVIykpalC7Np/YT2brlEj26/dL7ld5H1kctQQ3rQ2nwXHYSGvR7tXT/zeKxBe93LgFrl6JXCgmnn3N9KSrpq5x2SvuSce8E596ikhyW9qaixYXT08pdpPy4pxrWj9mdKk1SACFYCSfsBJM0f+WDjlSiN5onI5iydZhiTHD8obTOXoKjXtZvmMp30ooZ0p9c+6oNbWS+dFx2EdnreeT0WKQvpFfmzCAT1I2f6d8zsfZJmJH3IOXdM0oSkewL7POltA7rSy5J2/WjsEvWH1KT5BTYzjx8NrQwRFAwq05ZWiwquorb7peDiFkyG1RdOmsud5PidRC0iDJpzLjKHtaj6yP2qIR22kDdsDElyenud91t0EBr1nuT9WIPSOKpsqFWOXuh1O/FPSTpH0jpJT0v6eNoDmNkHzGzGzGaOHAmv8woEhV2KL0I/LikmmUW+cdNaXXXRqtgZ6uBtvZrNCesUGLc97Qxjkhn5MLVqRVdeuLJjush4rdrzHNZ+XbaOuwLij+Hic5d3fD36kfdbdKpXktSiPB6LlAWgvHoaTDvnvuecm3POnZD0pzqZylGXtDKw65netrBj3Oycm3TOTS5fvrzYAQMp9OOSYtI/sDduWqtHtr1NV120KvQ4V164csH3vfgAct1l56laWRjwVium6y47L3T/tDOMWVI4/Pfsxk1rF6SLtIfltWpFZup5Dmu/LlvHzaz6Y7j7oSMdX49+5P0WHYS2pxaFnSt5PBYpC0B59TTNw8zOcM497X37a5L8Sh93SPqimX1C0gpJr5V0Xy/HBuQhj0uKaS6Dt6exjC+tyjnpmp0HtGP3oUX39Uut5Vm/OColotPMcNoUnLSXuScSXH5vH2+w9mxYZ8LgOK/ZeSD0OEXnsPbjsnXUax+s15vk9ehH3m8vUr06nSt5PRYpC0A5FRZMm9ktkt4s6VVm9qSk6yS92czWqbXY+TFJvyVJzrkHzexWSd+W9JKkDzrnsi+xx0jK649YP2u5Tu+va+rLD8x3yqvPNnT1zgO6eucBTUSMxf8DmzSnOKp+cVZXXrgyNCe7fbbbF3x9T6tVlTQbIy6XO+yYx443F3VS7PQ80hilHNYkefRJXo9+vWa9DEIJeIHRU2Q1jyudc2c456rOuTOdc592zv26c26tc+71zrm3B2ap5Zz7qHPuHOfcGufc14oaF4ZTXrmY/a7lesNfPbig5XRQp7H0q3TW5FmnJ97e/vrONpo6dryZ6LWOuswtKfSYUiuQ9mP1qJi9YhbbpjvqnLj43OUjk8OaJMUgSToFeb8AhhHtxDEU8mob2+/2s0kqUESN5ewtd4bOwpqkR7df2v3gIqy74S7NNpqLto/Xqos6/iVt2R01Cx8m6TGXeSkwzzaaqa44xJ0TUxvX0JEuoIzVPAAgL7QTx1DLKxdzEGq5Ro2l20voWYOcsEA6anvS1zFNC/Okxzx2vKlataKbNq9bcMywFJHZ4ycD7rhzgkv6CyV5PXjNAAwbgmkMhbxyMfuV0+kHdEm01/ONyxHudAndP059trHgvmmC2TSS1OT1BVNU4oL802rVyIA+6pjBxWLBXODgceqzDU19+YHI4w9jbjQAIL1e15kGCpE0F7NTB71+5HR2agnePpb2er5ROcKdSme1P257ikjSfOs09aLTtvv2g/q4HPa05aSDM82d6ic355yacyfI8wUARCKYxlBIskAqyeLCftRyjQvoxmtVLVtaXTCWsHq+QU4n86rjxt0pkJSSpVCkqRfd/vr6zy9Kxazjokr/g0RSwRnlJM/vuRfnqO8LAIhEmgeGRlQuZjCVoV37Zf9+iGsJ3r6AT4qu55vkmGn3SZLKkLaOb9J21bVqJTLYD449qs61mXTqkkqmcm5JxpxUmlx0FucBwOAhmMZAyBpkhAVp7YKBWdJazXmOPW2edpIAMEkQ3Ok4aVIZul1UFhWQR30ICuaNR3U6dE7advna2Nd+auOaBXW9w4zXomfOO0lzPhVx7gFAXviwH41gGoXI84eumyAjSSpDMPCMq9WctQFMp7EnaYgRFLZ/UNZFh5Lmv09Tmi6JJOdDVEAe9tr4eeNx7+3EeC1ZkB9THbQ6Zrr+7eHtzZNIcz7lee71648ef2yB4cSH/XgE08hd3j903QQZnVIZqmO2IPDMuzRekrFnSZMI7h8s5+Z/3d5O/Nrpg/MtxIP8BYtOrRlY/zhJJA2cujkf4mas4wLppLPqO3YfUvNEeDSdxweKNOdTXudev/7o8ccWGF55TzQNG4Jp5C7vH7pugoyOKRFtlSCylMaLCyqTjj1tmkSSvGM/mLlt5gntfeRo5LH8QPqFl050DISylNLr9nwIe65xeePBILhTwJ/lQ1Ka2dc051NeZRn79UePP7bA8BqEHgz9RDUP5C7vH7q43OFOLj53eeztzTm3oDJE2tJ4nSqEdDP2tKKCmbhA2jfbaHasmpG1lF4Rv4SjXr9gFZMk1Vvi3oew/dO2m09zPkWdq53O4Xb9+qPHH1tgePXyb9kgIphG7vL+oeum9vPdDx3puE/wj31Y6bZTq2O6ZueB0LrUcbNxacbeqf51EkUELfXZxvx4kuSfB/f3FfFLeGrjGlXH2srxtaXsdHpv/OPE1b329/ffn6t3Huh4zKA0pRajztUk53BQv/7o8ccWGF796MEwSAimkbu8f+i6qf2cpfzbpvUT2rvlEt20eZ1eeOmEjh1vRs5CdpqNy1r/+pqdB7Q6ZWBdVNASbJySZn9/3IX9Em5v1uJ97we+UeON+vAUJcnzjzvP/PPp0e2Xxtb+zmtmt19/9PhjCwyvfvRgGCTkTCN3aRfUJT1mlvt3U/4tSQ5okjzXTmMPe5wsbb2jqoJ0mk1OotGc05hJEWv1Qvf3X6cizocduw8tKmfXnHO64a8e1PPNE7HPOezD06b1E1p3w12hbcPNez5x8vggk1fOdBGvd5kfF0BvdFsCdZiZi6jROggmJyfdzMxMv4eBEgurM+0vnFu2tCrnpGcbzQV/+OOavPj3f3T7pfPHb69TXK2Ydrzz/PlfOp0WrJ295c646mzzklSXCHusuOdStLxL7PmSvmbtqmOmHe86P3Q86//grtTdFKXWB5Y8ZmiiGtcw+wMA5WBm+5xzk+3bmZnGUIuaLZMUWvli5vGjun1fPdXM5qKoLvB9knJhSbvw+ekfV+88EBmkRs0cTN32QGgJuPY607VqRacsGQudoc0ia3m0rI1uOmpPDQlIWhIwKM8PC8zsAsBgYmYaIykqpzaqNbWvfaYw6jh+VYlOt0ve7HZEsBun06xl3Ay7P3sudf6g4T9WN0H2eK2ql5+yJHFL7U4ztFH7JBlj8LUPinqvli2tLkodYcZ4sNBMBkAemJnGyAr7Qxq1qCsukA6bhey0aCzu9k7pJJ2E1fCN624Y1JxzsTPc7bWp37DqNElKVGYvzGyjOR/kdpqtTtroZubxo/ONaCpmuuKCCU2edXqq9vFBUTnn11123vy4CMYGD81kABSNYBpDLeoPaa06puPNE4v2jwpAly2ths5mdlo0FnX7abVqx6AvieCx259rknnusNSRmcePLgqa9z5yNC5DIrW4Zh5JqlpM76/r9n31+Q8/c87p9n11TZ51urZdvjb2Q0p7mk7ww9ZpXinE2ePNRUEzgddgopkMgKJRGg8DJ01N5qg/pI2XFgfSUnQAeux4M/SxOpUDi7rdrHOFCH/fpJLUgQ7TXjnki/c+EbtfmDTj9EUFzUnqFXcKkPZuuUSf3LyuY6m29rKEs42mnm+e0E2b18WWscPgoJkMgKIxM42BEjbTHLYor1MKRZalAsHLw9LJy/7jS6s6ZcnYoqog0uJFZeNeBZEkVSMmUlbjyCM4yBKM+ykWfspFUlEzxGEpKu1BcJIAKcmCPmYth19eJQcBIArBNAZKkprMSSpydFpoGKXRnFtUy/jY8aZq1Ypu2rwuNADzK2yELZqL4geP/n3P2frV0PFW7GTyRacKF3E51N246DXLFqRcJBE1QxxMUfHHG5bXnTRA6lQXlVnL4ReVC08zGQB5IZhGobpZRZ9m4aCv0ZzrOENqil9o2EnYrHLcYkB//M+98FJsIN1e//qanQe0Y/chTW1coysvXKnP37M4/eLKC1fOfx0WNLQHpNLiSh1pXHXRqgWL/q68cKXufuhIquNVzBZVwoj6kBRVeSOvAGlYZy2pXnESJQcBFI1gGoXpZhV91H2Xvqyi516MD9w6BdJFFYNsXyDXPv44UcGu/7y3Xb5WkhYFsjduWjt/jDRBQ5KKH+3Ga1XduGntgseUWg1UooSla4SVlIt6faK25xUgDeOsJdUrFqNzG4AiUWcahUlSYzntfZMEf1EpHFlTO5KOIfi8osYfJsn9krxmWaQtzxeWchE3Zj/nu1PAG5fG8si2t6V4RskEZ27HIzphllWnWeden0MAMCqoMz3kynhZt5t81Kh9OoXCtWpFV1wwsShnulatdF2GLjiG9uMlXSAXNt6pjWs6BrXtx4t6v9NuTytsljNudjeYL75j96EFqSvBx4/6kJPHh5927TO3nXLei5bmvUky60weOAD0FsH0ECjrZd2ofFQnaf0f3BU6G+gHFlEhVNzscnDWdPKs0xcFKN00SGkXrF7hV7MIvtZRdaxfVjEtf+WpHTsOtlsxXtO10wdD88Hrsw1N3faAZh4/qp33HZ7vpJhke6eFmmHCGqhI0SkXSc7PuKsJUbJ+QOh1BY+4cab92U0y9mHNAweAsiLNYwiU9bJu2uoVYTPKSfbx83Cl+BzarG27o8YSt9DvmlsPhJbfM7WCmjRBfq1a0RtWnZa5+2BRorontktyfq6Oybv+5OZ1iVuet+djBz+A+HnmX7jnidAPaybp0e2Xzn+fx+LZqDJ//jjT/uyeveXOjmNP0o4dAJAeaR5DrKyXdYMzlp1mhDtV4eg06yyFL9wLjkOS8mrjF1ee75qdByJn1p1OLqqrzzZ09c4DsY8zH5x32K8fkl4BSXJ+TkTMpo63dYr0H/OUJWMdZ2ivnT64oALKnHP6/D1PaGnEVYPgzG2ei2fbz4XgOJP+7Ha6YhMcO9UrAKC3CKaHQJkv6/o5s1EzakFRgbRJC2bpwlbmb9i+p2NwtWP3ITXniluY6CviWk9Zrx8lSY9Icn5G5V2HdYpsNOcir14Eg9Bb7j0cPuaXTnTMee8mFSRJJ0p/nElem05XeMKqj1C9AgB6h3biQ6BTS+sySBLYR+XHJrlvkhm+qH2KWOSWl/aui0XL0ha801WHJOfnpvUT2nb5Wk2M12RqzVRvu3ytZhN0igwKnitR76tzCn2sYPBZxOLZsHEmeW3igvOwsQMAeouZ6SEwCJd1w2Yeg+LyoZN8KEgywxe1T94z02G6eYy8qpB04qeUTN12QCFZEJHiFglKrfNz5vGj82k8YyZJblFlj7DZ1KgUoWVLqwu6UEqLK6PEjbfTzG03V3s6daIMntNJfnajgvP2KzYAgP4gmB4SZb+s2x40RNX2DcuHTvK8kjTfuPjc5aFdBLsNpDvVvh6vVXXgurdo3Q13abaRbqa1l56abejDf3kwVSAtdX79pvfXF7QbP+Gkhvcg7TPv7e991Pt63WXnhe4vda6MEuwaGaWbZi5JOlEGz+kiA3sAQPGo5oGulKm+ddZmFt3wK0TELZ5ctrSq/R95i6b31zsuOIwKzJctrYa2MS9S0u6IcVVj2hcBpnm8TlVawip13P3Qkcj3OKxrZJw8qnnk8XNBdQ4AKIeoah4E08hs0P7IJ1kEmUWShjAT4zWt/olabHm76php85tWLqgJ7W9fUrH52dwwYyb9mwtXaec3D4cussxqYrymi89dHvlhIe79ThNIxz1+MFBP27HR1172btCU6UMrAIwqgmnkroj61kUGDUXNTOeRb+3Pmk6edbqmvvzAgoC4WrHEAbJJWvqyip57Mb886041taPen7w+vEykSOGIO8Yg5BcTNANAeVFnGrnLu7510Z0cOy2CzCKvhYt+DeSwmdw0M81OyjWQrphF1tT+7rPPa+bxo4valZ9Wq8osv3J+cbWlw3Qqe1dWZe1kCgCIR2k8ZBa1ACrrwqi42r55CJZfQzJxHxb8DwDv/dN/0NSXH1B9tiEnabbRzD2/u9GcS7R4s2LWsexdWRV9/gMAisHM9IjK43JyNxUPwvSik6NfOSGufTXSybPNedJFj1GuvHBl6SvbRClrJ1MAQDxmpkeQfznZn0n0LydP76+nOk5Uo42sgUzeM90opw3nnD5/zozXqlq2tDp//ty0eV2mKwcVM1110arElTrKiPMfAAYTM9MjqJtWye3ynAXMe6Y7TrczoMjusR80QhcDBit1hJXJi8uXfmTb2/IfaI/18vxHf/VjoSmLW4HiEEwPgLx/CXZ7OTnreKLuF9w+vrSqU5aMLWrmEnUcf7Hb7PFm5DHDjkMg3T/12Yam99cXvB/T++sLqpgE3x+/msf1dzwYmjc9XqsWPeSeGIROpkUZpUCvHwtNWdwKFIvSeCVXRC3nbkraZR1P1P2uuGBiUW3kasW0453nhx4v7DhB/oxm+8xmezm3LLWK0xiUme8xa3Uk7LX2c2b9H9wVumjRb3gjeQH3bQ8sqsG9413h50pSoxTIldGg1avvVhElRcv4mMAwiiqNR850yRWxwn9q4xrVqpUF25JeTk47nun9dW3YvkdX7zwQer8v3vvEotJvzTmnG/7qwcSPH+Ta/m/fXp9taOrLD+jic5erWrHI43SjOmYDEUhLiwPpypjptT/58kzHSvNqtp8zUdU/jh1vzp9D1+w8oJefsmRBjnUegXQe6weQ3ahVMenHQlMWtwLFIpguuSJ+CXazcDBqNrc+29CG7XsWBCHBQCVK1KxoVHCVxy//5pzTX95fL2zquNmPqd6czJ1w+qdnnkt9P3/x4Cc3r0v8SyXpexkMdmcbTT3fPKGbNq+bv8Jw9pY7F517SY1aIFdGoxbo9WOhKYtbgWKRM11yK8ZrocFot78Esy4cjOv4156H12kWOYuo1yOtPBubjLL2y/Ebtu9RdNPzhcaXnsx1Hq9VQ/OhTQoNdq+/40G98NKJrnNARy2QK6OifseVVT8WmrK4FSgWM9MllyUlw78s3s2MnSRdO31Q52z9qlZvuVPnbP2qrp0+2LHjnz+rN72/3nXQGzb2sNcD/RF2RSPNe/5C4A/79W8/T9WxxYkiUWfbbKOZy4wyM3b9103a2SDKu6RoWR8TGCUsQBwAaRZI5bWY59rpg6GtrWvVMTWaneceO5UySypsgVnw9Ti1OqYXXjrRl0V0gybvxYaPbb90/utgWbs8jpF1AadJejRwzE5GbfFbWbEIFMAgiFqAWFgwbWZ/JulXJT3jnHudt+10STslrZb0mKR3O+eOmZlJ+kNJb5N0XNK/dc7d3+kxRiWYTiOvVdvnbP1q6Cz0mEmnLIkPlONSQbIYr1V14Lq3LNreqbIHFqpWbNFiz6zMpEe3tYLWbt6Hx0IC36hz2FerVnRqdSw0rz5LdQICOQBAElHBdJE505+R9CeSPhfYtkXSN5xz281si/f970n6FUmv9f5dKOlT3v9IKa8c0Khg+ISTtl2+NnNzjSzac2mzzoKOuuac08tfVsklXzx4emTNjY+qDx13rvqlDSXllgM6qO3HAQDlUFgw7Zz7WzNb3bb5HZLe7H39WUl/rVYw/Q5Jn3OtafJ7zGzczM5wzj1d1PiGVV6LeaJmlytmC4KPa6cP6pZ7D2vOOVXMdMUFE7r7oSO5B7o/8x+/RjpHDp57cU6VMdNcDi/kuhvukll05ZU41THT9W8/L/S2qHM4bNaZGWUAQL/1uprHqwMB8nclvdr7ekLS4cB+T3rbCKZTymvV9pUXrgzNmb7ywpXzX0/vr+v2ffX5oHvOOd2+r64rLpjQ7fvquc5QJ8nTRjJ5BNLS4isGSdW8PPerdx7Qh259QFdeuFI3blrbsZ14+znMjHL55N0dFQAGQd9K4znnnJml/qtuZh+Q9AFJWrVqVe7jGnR5tSS+cdNaSVow6+wHPb6oGr13P3RE2y5fG9n+uV11TCJWHh3BD0Zzzunz9zyhR4/8WPc/8ez8+RTsYjnutYy/ZucB7dh9iECrpLK2rKbVNYBB1+tg+nt++oaZnSHpGW97XdLKwH5netsWcc7dLOlmqbUAscjBDqq0M3bBWaHxpVU5Jz3baGrFeE0ff3d0h7lO+dk/ev6lyMdsz31tnmAR4Sjb+8jRRdv8QDqPetK9FjbTKg13WkpcA5y455n1fgBQFr0Opu+Q9H5J273/vxLY/jtm9iW1Fh4+S750Z2kvjQb3P82b7Tt2vLngknow/zUYuMw8fnR+ltpMqi0ZiyxdNr60qq27OteklrIvXsNoCLuyUfZAK2ymdeq2ByTTfDWVQflQkEbWxc80zgEw6AoLps3sFrUWG77KzJ6UdJ1aQfStZvabkh6X9G5v96+qVRbvYbVK4/1GUeMaFmkvjbbvHwxS4kLeRnNOW3d9a8Gleeek4xF5GbVqRc4t7lrXzh8vgTSyaA+0ypRzG/YBMazFfNk/FKSVdfHzqHVABDB8iqzmcWXETb8Ysq+T9MGixjKM0l4a7WYGOOniPz9145qdBxIel0AaJ1XHbEHQWR0zveLUJaHVQlaM1yIXLPZ71jfNjOowzb5mXfxMq2sAg4524gMq7aXRov9om6S9Wy7RpvUTzCghtWVLq62TKMikS19/Rmir6YvPXa6tuw7Oz2i2z/tmaS2elzTn/zD9rGRtWU2rawCDrm/VPNCdpJdG/dm7rCs1kzZhCT7u1MY1mrrtgdBL2+iPrO25e8FPDWrvzticc7r7oSN6w6rTFixQfMOq03T3Q0c6npf9mvUNm2mtjtmCnGlpOGdfs5YrpMwhgEHGzPSAmtq4JnTGLvjH2c+TTtNAZcxadYCDM0SdhAYF7bOM6Cu/1FwZjNeqi2Yhn40ooVifbSyq9LH3kaOJzul+zfqGzbTueNf52vHO85l9BYAhxMz0gEpSTzpLnnRrMtl00+Z188f60K0PRFbmmIh43PZZRvRfr9+R9rJ2UuuD1/VvP29REJl3e/h+z/pGzbQSPAPA8CGYHmCdLo1GXeY2STdtXhcZwLQvZExS4k7SggVhGG1+0Cwlq60ctQgt7sNg++1+KkvYBzwAAIpiLmGgVEaTk5NuZmam38MorQ3b90QGtsHKG1FngL9P3My0dDKIKXNeLoq1rK3ZT5ZgNqy8XdS5VzHTx999fmnK4QEAhp+Z7XPOTbZvZ2Z6iIXN9vn88mFLX1bRcy+Gz/75+3SamXZt/2P0HDveVLWyMD1ISlf/OexKy8zjR/X5e55YtO+VF65ctC3LYwIA0C1mpodcp9QLZpORp2VLq9r/kbdIWtwoSGqlZqRdeHft9MH57psVM1154UpNnnV66LGvuGBCt++rd/2YAAC0i5qZJpgeAHnMtK3ecmdBowMWemz7pZKi04wmxmvau+WSrh4j6tgVs9ArKXk8JgBgtJHmMaDStg2PEhVkAEUpsoFQ1DGizvFh6jQIACgXgumSS9s2XFo4k31arSqz+Ioc1YpRyg6hzKQ0n8HGa9WOjYKcWqkbN27qXMM8SlTToqgPjcPUaRAAUC4E0yUXN7sXlv4hacFM9mxEMwzfeK2qHz4fvw9GV5pAujpm+tXzz4hc9BrkLyrMGlBHldKLypketk6DJM++0wAAFpZJREFUAIDyIGe65KJyQ8MaYrCYEL308pdVdPzFOTlpfmHg3Q8dSVxnvGKmR7a9LfS2JOsEovahmgcAoAgsQBxQURURTq2O6dhxZpTRH1HdDdN23PQXKwblVQUEAIA8sQBxQEW1Db9m54E+jwzDJO1VjbD0obSBdMUstOzd3Q8dSb1OAACAfiGYHlBRC7CAtGrVisZMkc17ivKa5UsXNGSZcy60QYuPihwAgDIa6/cAEM+/5F2fbcjpZGm8i89drlq10u/hYcBNjNe07fK1Ot6DQLpiNv//VRet0j8fOZ7q/lTkAACUETPTJRdVGu/uh45o2+Vr57sbsvgQab2sYvONTOK6ZC5bWpVznSvD1KoVPd+cCz0Px0yLFhvGzUK3519TkQMAUFbMTJdcXGm8TesntHfLJXps+6W6afM6TTBzhxReDNQWn9q4ZtGVjlq1ok9uXqf9H3mLDlz3FlnMsfwZ7vdetCr09n9z4eLtYxEHHDNp2+VrNTFekwWOTb40AKCMmJkuuajc6PZL3pvWT2jT+onIUnphslRfwHBpb/BzanVMs8ebGvdmo6/ZeUA7dh/S1MY1kedisFW3H/C2LyoMqyd9ypIxNZonQrf75zMAAGVHMF1yUxvXaOrLDyzoUFit2KJL3u/903/Q3keOJj7uhFcVZNtXv63v/ejF3MaLwXLNzgPzaRmzjaZq1Yree9GqBY1P/Dz9pA1Rbty0NlEzludDAum47QAAlBFpHoOgPQm17fu0gfSypVVNbVyjHbsPEUiPuPZTq9Gc0y33Ho7N088r/SJqQSELDQEAg4SZ6T7r1MUt7LJ684RbUHM3TSAtSceON3U1daoRYS6ikZOfp59X+kVUS3AWGgIABgnBdB+1d3rzL6fPPH500eX0dtTcRVEqZqEBdd4zxlENiciVBgAMEoLpPooqexdXMsyXNLChZB7Suug1y3T/E8/2ZMaYhYYAgEFHMN1HWWeXTa1Z7PV/cJcirsjPI5BGWg8+9aP5GubMGAMAEI9guo+ytgT3A+Rjx+ObaABZzDaazBgDAJAQ1Tz6KKxRRpRataKlVd4uAACAMmFmuo+CC7DiZqgrZrrigolEudRAt5YtrUZWmQEAAAuZ65R0W2KTk5NuZmam38PIRXtlj3Z0K0S3qhXT5jeu1M5vHl7QBChsn7DmLO01pQm4AQCjxMz2Oecm27eTN1ASm9ZPzDfECEMgjSQs7kYnTZ51una88/z5xivLllY1XqvON2HZ8c7zdfdDR0KrzOzYfWj+e//DX322IaeTZR2n99cLeFYAAJQXwXSJbFo/ob1bLun3MDDATqtVtWxpNfQ2v9lPkHOStUXgUVVmgtujyjq2Hx8AgGFHznSfhV0qj2qaAXQy22jGLmr1Z5D9QHi20Vx022m16oLtvmBt8yQBNwAAo4CZ6T6KulQeF0hXK7EX8gE1mnOqtE83eypmsSlDjeaczLQoIG9v2hLVNCjvLokAAJQdwXQfRV0qjwqEli2t0oUFicw5FxoQJ7niMXu8OZ+/7+dSty8+DCvrWFSXRAAAyow0jz6KuiTuB0LBQNtEkxYkt2xpVddddt6iFKJOZRil1uxyp6YtwbKOVPMAAIwyguk+iuqAONEW+JiYkEY6zikyII4rwShJF5+7PNFj0CURAADSPPoq7lK5X9ljYrxGII3Unm1bQDi9v64N2/fomp0HdMqSsciKH5J090NHunps/7HO3nKnNmzfQ7k8AMBQY2a6j9ovlY8vrco56ZqdB7Rj9yFNbVxDdQRkElwI2N4QqFPFj27OufbH8hfVSmIWGwAwlJiZ7jN/Bvqmzev0fPOEZhvN+coe1+w8wKw0Yi1bWu24EDDtQtduKnJQfxoAMGqYmS6JsCCEQBpxatWKrrvsPEnxCwHTLHTttiIH9acBAKOGYLrP/KYtnSosYLhMjNd08bnLdcu9h2PL1Y2ZdMqSMT3fPKFTq2N64aUTOuFa9aKvuODkAsC4FIokC13zqsgR9VjUnwYADCuC6T5qzy/F6KjPNrTzvvhAetnSqvZ/5C2STp4rJ7zd55zT7fvqmjzr9I7B79TGNZq67QE1T5x8rOqYzQfOeeYyT21cs+icpv40AGCYkTPdR2GpHRgNJi0IbsME4+yoXOSrdx5IVjGjPT26oEaam9ZPdGz4AgDAMGFmuo/IIx1dSfLhg+Xt4s6VThUzduw+pObcwkdszjnt2H2okCCX+tMAgFHCzHQfjcfU+gWCecadco7jKmawKBAAgOIwM90j/kLD4EKvmHRZjLj2POOwXOR2UcExiwIBAChOX2amzewxMztoZgfMbMbbdrqZfd3M/sn7f1k/xlYEf/FYfbaxoIb0bFuXOoyWaiU8cTkszziYixwlKjiO67QJAAC60880j4udc+ucc5Pe91skfcM591pJ3/C+HwrUkEa7WnVMO955/vxCvVp1TGNebP3dZ5/XzONHF93Hb/Dzyc3rFgXHptaHtLDFiCwKBACgOGVK83iHpDd7X39W0l9L+r1+DSZP5Kai3anVyvxCvWunD+rz9zwxf9ucc/Pf37hp7aL7BtvQ12cbMp38cBa1GJFFgQAAFKNfM9NO0l1mts/MPuBte7Vz7mnv6+9KenV/hpY/clPRbvb4yRSfW+49HLpP1PZ27Vc5/MWI0/vr2rB9j87ecmey8nkAACC1fs1M/7xzrm5mPynp62b2UPBG55wzs9BMCC/4/oAkrVq1qviRdoHuhogSrOQS1bglanuSZj/+DLW/T6fyeQAAIJu+zEw75+re/89I+ktJb5L0PTM7Q5K8/5+JuO/NzrlJ59zk8uXLezXk1IKLDoF23VRySdLsp2IW2uQlqnweAADIpufBtJm93Mxe6X8t6S2S/lHSHZLe7+32fklf6fXY8kR3Q8R5totKLp1y8GvVSuSsNvn7AADkqx8z06+W9Pdm9oCk+yTd6Zz775K2S/plM/snSb/kfT+wCFoQJ5jmEVXuLmp7XA6+X6kjy30BAEB65ga4c8jk5KSbmZnp9zBCbdi+hxQPxJrwmvdIWpQDXatWtO3yViWP9mY/cfv7+dBhedXt+wAAgOTMbF+gpPM82okXJKxRBhAUXBQYVgda0qJmP3H7RzV5obY0AADFYWa6QFTzQBIT4zXt3XLJou1RVzei9gcAAMWJmpkuU9OWoTPz+FF999nn+z0M9FCtWkm98DQqvz7t9l7zPywGU1CY+QYAjBqC6YK0d7XDaNh2+drUVyPaFwX6QWrUNaO4RYRJAtw8guD2nGzqWAMARhXBdE6unT6oW+49rDnnZNZdHWEMLr9td9gCwOqYac45nQicG9Ux09TGNQtSgoLtwcNcfG54ffUkAW5eQXBY6Ue/jjXBNABglLAAMQf+LLRf25dAejRVAz9NYQsAN79ppSpjtvBO1koHCjb46XT63P3QkdDtcQFumn2SKHsKCgAAvcLMdA6+QDoHJL3i1OqC7/1Zat+G7XvUnFsYKjfn3PwVjaS6ybHOKwheMV4LTWWhjjUAYNQwM50DJqIHU94n/+zx+K6GUQFrmkBaig5Yk2xPe98oYaUfa9XKfB1sAABGBcE0RtZpS6sar1Ujbz9lyZiq7WkZMaIC0un9dW3YvifyQ1fFkj9GXMCaJMDNKwimjjUAAC2keWBkHTveVK1a0VUXrdIt9x3WXGBlYGXM9K7JM7Xzm4cTHSsqIA1biNh+vysumNDt++oL9vEXIY7XqjJrzXp3qrzhb4+r1JFkn6Ta01gAABhFNG3JAWXwBpvf1rs9wIwqcRe1v1/FI7j9+Isv6VhE+sdEzP2o2QwAQLlENW0hmM7JL3/ir/VPzzzX72EgA5P06PZLF20/e8udoakZUft3moVOcgwAAFBOdEAs0LXTBwmkB1jcorwkFSuytI3vtvEKAAAoBxYg5oDSeIOr2wV9/mx0mkBa6tx4pT7bkNPJpirT++upjg8AAHqDYDoHg5soM9o6VaBIUrEirAlKEt00XgEAAOVBmgeGVnXM1DwR/lHHJO3dcknHY3SqWJG14183jVcAAEB5EEynFJbP+vKXVfTci+lnJ1GsqEBayq9TX1RetdSayY6q5tFtnjYAACgH0jxSiMpn/bU3TKiSorkH+ivPTn1RedWf3LxOe7dcousuOy9VkxQ6CwIAMFgIplOIyme9+6Ej+vi7ztcEs4c90c1Jm3envk551Wk7BdJZEACAwUKd6RSS1B1eveXOno1nVC1bWpVz0mwjvBlKnMeo7QwAADKIqjPNzHQKcXmu6J1jx5t64aUT+uTmdfrk5nXzs7jjtaqWLa32e3gAAGCEsAAxhamNaxZ1uPPzWf2FiegNv1zc3i2XLEqBWHPt1/TCSycW3eeUJXx2BAAA+SK6SCEqn1WSPnTbA6kbdyBakuWcUeXiPnbF69W+HnTMWtsBAADyxMx0F2aPv6hrdh6gaUsBkrymUek1/kw1LbkBAEDRCKZT8Evj+Wke1Jbur6iW3FLnZisAAAB5IM0jhayto1GMqJbcAAAAvUIwnQItnYuRtd0N7wcAAOg3gukUKIGXv4nxmm7yytulxfsBAAD6jWA6hbBWz+jOU7MNbVo/ob1bLtFVF60K3WfDOafTYhsAAJQSwXQKwdJ4yIeTtGH7Hk3vr+vGTWt11UWrVLFW4kfFTFddtEpf+F9/jhbbAACglGgnnlFUa3G02n0/3zyxYLFmtWKSk5onwl+1WrVCgAwAAEqLduI5G7V83aSLBE2tdt+nVsc0XqvOzyTveOf52vymlf9/e/cfI8VZx3H88+lxbU6LuRoIKVciRAkNxPSkZ9NEMaV/yI+kUk21NEZbY4ImoDaxJFRNyn+aEK0xqZgaCTViaWMBSUSxqSagtpXjh6WUnJKWCicFGqVtAqVAv/6xs3Q4dm9vp9udmb33K7ns7DO7e99Jnjz7yewzz1w86zxS9Y6GAAAAZcI60xnNv36yfvXMv/Muo21ClaA82tn49P7/nT538fkrr72pex/b1/D9rM4BAADKhjPTGT2x+2jeJbRdo2ktI/dXn19IphI1ev94O9sPAADKjzCd0Zlzb+ddQktVJ1/09fbox3f2Z177OStW5wAAAGXENA+or7dHKxfMuuTivzXbhzRcZ9rFyOkaPd1dumrCFTp15lzL/j8AAEAZEKabtGXvcMdcKNfdZa2544ZLQmz1+IZPnak7xzk9f7oahCXp/k37m7rdOit4AACAsiNMN2HL3uGmA2NRVEPvmu1D+s+pM5pa42zwyOMb7aLDapD+66pbL2lvFMRHhnCCNAAAKDPCdBPWbB8qZZCW3rnT4GjhtdbxNbP6Rvrz02e4u2xdiCBAAwCAjkOYbkKZl24by0oZzR7faJ/ZKLgDAAB0AlbzaEJZl24b60oZ9Y7vmvd1q6e7K9NnAgAAdDLCdBNWLphVM1QWwcg60kvdjfUiv3rH98Btc/T9z31Ufb09F+9oyIWDAAAATPNoSjU81rqIb/qq3+VWV29Pt1Z/Zs6oFxeOxWjHl94PAACACkc0ui9dcQ0MDMTg4GDeZbznvrdlvx599sjFOwmmdV9hrfn8DQRdAACA95Dt3RExcFk7YbpcqqtkvJsz0AAAAGhOvTDNNI+SYZUMAACA4uACRAAAACCjwoVp2wttD9k+ZHtV3vUAAAAA9RQqTNvukvSQpEWSZku6y/bsfKsCAAAAaitUmJZ0k6RDEfFiRLwlaaOkJTnXBAAAANRUtDDdJ+lI6vnRpA0AAAAonKKF6YZsL7M9aHvw5MmTeZcDAACAcaxoYXpY0rTU8+uStosi4uGIGIiIgcmTJ7e1OAAAACCtaGF6l6SZtmfYvlLSUklbc64JAAAAqKlQN22JiPO2V0jaLqlL0rqIOJBzWQAAAEBNhQrTkhQR2yRty7sOAAAAoJGiTfMAAAAASoMwDQAAAGREmAYAAAAyIkwDAAAAGRGmAQAAgIwcEXnXkJntk5JebuO/nCTp1Tb+P3Qu+hJaif6EVqI/oZU6qT99KCIuu2NgqcN0u9kejIiBvOtA+dGX0Er0J7QS/QmtNB76E9M8AAAAgIwI0wAAAEBGhOnmPJx3AegY9CW0Ev0JrUR/Qit1fH9izjQAAACQEWemAQAAgIwI02Nge6HtIduHbK/Kux6Uj+3Dtvfb3md7MGn7oO0nbf8rebwm7zpRTLbX2T5h+/lUW83+44qfJOPVc7bn5lc5iqhOf1ptezgZo/bZXpzad3/Sn4ZsL8inahSR7Wm2/2z7BdsHbH8raR9X4xNhugHbXZIekrRI0mxJd9menW9VKKn5EdGfWiJolaSnImKmpKeS50At6yUtHNFWr/8skjQz+VsmaW2bakR5rNfl/UmSHkzGqP6I2CZJyffdUklzkvf8NPleBCTpvKRvR8RsSTdLWp70mXE1PhGmG7tJ0qGIeDEi3pK0UdKSnGtCZ1gi6ZFk+xFJt+dYCwosInZI+u+I5nr9Z4mkX0bFM5J6bV/bnkpRBnX6Uz1LJG2MiLMR8ZKkQ6p8LwKKiGMRsSfZfkPSQUl9GmfjE2G6sT5JR1LPjyZtQDNC0h9t77a9LGmbEhHHku1XJE3JpzSUVL3+w5iFrFYkP72vS007oz9hTGxPl/QxSc9qnI1PhGmgPT4ZEXNV+Ylrue1PpXdGZVkdltZBJvQftMBaSR+W1C/pmKQf5lsOysT21ZKekHRvRLye3jcexifCdGPDkqalnl+XtAFjFhHDyeMJSZtV+Zn0ePXnreTxRH4VooTq9R/GLDQtIo5HxIWIeFvSz/XOVA76E0Zlu1uVIL0hIjYlzeNqfCJMN7ZL0kzbM2xfqcqFGFtzrgklYvv9tidWtyV9WtLzqvSju5OX3S3pt/lUiJKq13+2SvpyctX8zZJeS/3cCtQ0Yt7qZ1UZo6RKf1pq+yrbM1S5cOzv7a4PxWTbkn4h6WBE/Ci1a1yNTxPyLqDoIuK87RWStkvqkrQuIg7kXBbKZYqkzZUxRxMk/Toi/mB7l6THbX9V0suSvpBjjSgw249KukXSJNtHJT0g6Qeq3X+2SVqsyoVipyV9pe0Fo9Dq9KdbbPer8nP8YUlfk6SIOGD7cUkvqLJyw/KIuJBH3SikT0j6kqT9tvclbd/ROBufuAMiAAAAkBHTPAAAAICMCNMAAABARoRpAAAAICPCNAAAAJARYRoAAADIiDANAB3M9jbbvTXaV9u+L9m+x/bU1L7Dtie1s04AKCvCNAB0sIhYHBGnGrzsHklTG7wGAFADYRoASsz2StvfTLYftP2nZPtW2xvSZ5ltf9f2P23/RdKspO0OSQOSNtjeZ7sn+ehv2N5je7/t69t/ZABQDoRpACi3nZLmJdsDkq623Z207ai+yPaNkpZK6lflDmQfl6SI+I2kQUlfjIj+iDiTvOXViJgraa2k+9pxIABQRoRpACi33ZJutP0BSWclPa1KqJ6nStCumidpc0ScjojXJW1t8LmbUp8/vaUVA0AHmZB3AQCA7CLinO2XVJn3/DdJz0maL+kjkg6+i48+mzxeEN8VAFAXZ6YBoPx2qjIVY0ey/XVJeyMiUq/ZIel22z22J0q6LbXvDUkT21UsAHQSwjQAlN9OSddKejoijkt6U5dO8VBE7JH0mKR/SPq9pF2p3esl/WzEBYgAgDHwpScuAAAAAIwVZ6YBAACAjAjTAAAAQEaEaQAAACAjwjQAAACQEWEaAAAAyIgwDQAAAGREmAYAAAAyIkwDAAAAGf0f5me9zTIAAtoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean: 30.20, median: 22.00\n"
     ]
    }
   ],
   "source": [
    "def bbox_summary():\n",
    "  marks = json.loads(open(data_dir['train_label'], 'r').read())\n",
    "  bboxes = []\n",
    "\n",
    "  for img, mark in marks.items():\n",
    "    for i in range(len(mark['label'])):\n",
    "      bboxes.append([mark['left'][i], mark['top'][i], mark['width'][i], mark['height'][i]])\n",
    "\n",
    "  bboxes = np.array(bboxes)\n",
    "\n",
    "  fig, ax = plt.subplots(figsize=(12, 8))\n",
    "  ax.scatter(bboxes[:, 2], bboxes[:, 3])\n",
    "  ax.set_title('bbox width-height summary')\n",
    "  ax.set_xlabel('width')\n",
    "  ax.set_ylabel('height')\n",
    "  plt.show()\n",
    "  return np.mean(bboxes), np.median(bboxes)\n",
    "\n",
    "mean, median = bbox_summary()\n",
    "print('mean: %.2f, median: %.2f'%(mean, median))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 500
    },
    "colab_type": "code",
    "id": "TtWl8t0NjZZ8",
    "outputId": "63d3ae36-612e-430a-f48f-735cf34adae0"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 4444, 1: 12446, 2: 9532, 3: 7608, 4: 6720, 5: 6146, 6: 5141, 7: 5032, 8: 4510, 9: 4186}\n"
     ]
    }
   ],
   "source": [
    "def label__nums_summary():\n",
    "  marks = json.load(open(data_dir['train_label'], 'r'))\n",
    "  \n",
    "  dicts = {i: 0 for i in range(10)}\n",
    "  for img, mark in marks.items():\n",
    "    for lb in mark['label']:\n",
    "      dicts[lb] += 1\n",
    "\n",
    "  xticks = list(range(10))\n",
    "  # df = pd.DataFrame(dicts, columns=['label', 'nums'])\n",
    "  fig, ax = plt.subplots(figsize=(10, 8))\n",
    "  ax.bar(x=list(dicts.keys()), height=list(dicts.values()))\n",
    "  ax.set_xticks(xticks)\n",
    "  plt.show()\n",
    "  return dicts\n",
    "\n",
    "print(label__nums_summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "colab_type": "code",
    "id": "IjP2XN1AXIt5",
    "outputId": "aed34a79-d44d-4f15-da71-8adbe0002318"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1个数字的图片数目: 4636\n",
      "2个数字的图片数目: 16262\n",
      "3个数字的图片数目: 7813\n",
      "4个数字的图片数目: 1280\n",
      "5个数字的图片数目: 8\n",
      "6个数字的图片数目: 1\n"
     ]
    }
   ],
   "source": [
    "def label_summary():\n",
    "  marks = json.load(open(data_dir['train_label'], 'r'))\n",
    "  \n",
    "  dicts = {}\n",
    "  for img, mark in marks.items():\n",
    "    if len(mark['label']) not in dicts:\n",
    "      dicts[len(mark['label'])] = 0\n",
    "    dicts[len(mark['label'])] += 1\n",
    "\n",
    "\n",
    "  dicts = sorted(dicts.items(), key=lambda x: x[0])\n",
    "  for k, v in dicts:\n",
    "    print('%d个数字的图片数目: %d'%(k, v))\n",
    "\n",
    "label_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101
    },
    "colab_type": "code",
    "id": "S__NQlDNCkTf",
    "outputId": "72f230c7-e763-4448-8346-8b20f6808c3c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1个数字的图片数目: 1918\n",
      "2个数字的图片数目: 6393\n",
      "3个数字的图片数目: 1569\n",
      "4个数字的图片数目: 118\n",
      "5个数字的图片数目: 2\n"
     ]
    }
   ],
   "source": [
    "def label_summary():\n",
    "  marks = json.load(open(data_dir['val_label'], 'r'))\n",
    "  \n",
    "  dicts = {}\n",
    "  for img, mark in marks.items():\n",
    "    if len(mark['label']) not in dicts:\n",
    "      dicts[len(mark['label'])] = 0\n",
    "    dicts[len(mark['label'])] += 1\n",
    "\n",
    "\n",
    "  dicts = sorted(dicts.items(), key=lambda x: x[0])\n",
    "  for k, v in dicts:\n",
    "    print('%d个数字的图片数目: %d'%(k, v))\n",
    "\n",
    "label_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zQecTS1hXYnj"
   },
   "source": [
    "# 构建分类Baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FWLfntE1SiwU"
   },
   "source": [
    "## 网络配置信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4jgH1oAgSi9Z"
   },
   "outputs": [],
   "source": [
    "class Config:\n",
    "\n",
    "  batch_size = 64\n",
    "\n",
    "  lr = 1e-2\n",
    "\n",
    "  momentum = 0.9\n",
    "\n",
    "  weights_decay = 1e-4\n",
    "\n",
    "  class_num = 11\n",
    "\n",
    "  eval_interval = 1\n",
    "\n",
    "  checkpoint_interval = 1\n",
    "\n",
    "  print_interval = 50\n",
    "\n",
    "  checkpoints = 'drive/My Drive/Data/Datawhale-DigitsRecognition/checkpoints/'\n",
    "\n",
    "  pretrained = None#'/content/drive/My Drive/Data/Datawhale-DigitsRecognition/checkpoints/epoch-resnet18-13-bn-acc-58.34.pth'\n",
    "\n",
    "  start_epoch = 0\n",
    "\n",
    "  epoches = 100\n",
    "\n",
    "  smooth = 0.1\n",
    "\n",
    "  erase_prob = 0.5\n",
    "\n",
    "config = Config()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IwUo2Emm_V9z"
   },
   "source": [
    "## 构建数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-gXYWz5f_QYR"
   },
   "outputs": [],
   "source": [
    "class DigitsDataset(Dataset):\n",
    "    \"\"\"\n",
    "    \n",
    "    DigitsDataset\n",
    "\n",
    "    Params:\n",
    "      data_dir(string): data directory\n",
    "\n",
    "      label_path(string): label path\n",
    "\n",
    "      aug(bool): wheather do image augmentation, default: True\n",
    "    \"\"\"\n",
    "    def __init__(self, mode='train', size=(128, 256), aug=True):\n",
    "      super(DigitsDataset, self).__init__()\n",
    "      \n",
    "      self.aug = aug\n",
    "      self.size = size\n",
    "      self.mode = mode\n",
    "      self.width = 224\n",
    "      # self.weights = np.array(list(label__nums_summary().values()))\n",
    "      # self.weights = (np.sum(self.weights) - self.weights) / self.weights\n",
    "      # self.weights = t.from_numpy(self.weights.to_list().append(1)).float()\n",
    "      self.batch_count = 0\n",
    "      if mode == 'test':\n",
    "        self.imgs = glob(data_dir['test_data']+'*.png')\n",
    "        self.labels = None\n",
    "      else:\n",
    "        labels = json.load(open(data_dir['%s_label'%mode], 'r'))\n",
    "\n",
    "        imgs = glob(data_dir['%s_data'%mode]+'*.png')\n",
    "        self.imgs = [(img, labels[os.path.split(img)[-1]]) for img in imgs \\\n",
    "                if os.path.split(img)[-1] in labels]\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "      if self.mode != 'test':\n",
    "        img, label = self.imgs[idx]\n",
    "      else:\n",
    "        img = self.imgs[idx]\n",
    "        label = None\n",
    "      img = Image.open(img)\n",
    "      trans0 = [                \n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "      ]\n",
    "      min_size = self.size[0] if (img.size[1] / self.size[0]) < ((img.size[0] / self.size[1])) else self.size[1]\n",
    "      trans1 = [\n",
    "            transforms.Resize(128),    \n",
    "            transforms.CenterCrop((128, self.width))\n",
    "          ]\n",
    "      if self.aug:\n",
    "        trans1.extend([\n",
    "            transforms.ColorJitter(0.1, 0.1, 0.1),\n",
    "            transforms.RandomGrayscale(0.1),\n",
    "            transforms.RandomAffine(15,translate=(0.05, 0.1), shear=5)\n",
    "        ])\n",
    "      trans1.extend(trans0)\n",
    "      if self.mode != 'test':\n",
    "        return transforms.Compose(trans1)(img), t.tensor(label['label'][:4] + (4 - len(label['label']))*[10]).long()\n",
    "      else:\n",
    "        # trans1.append(transforms.RandomErasing(scale=(0.02, 0.1)))\n",
    "        return transforms.Compose(trans1)(img), self.imgs[idx]\n",
    "\n",
    "\n",
    "    def __len__(self):\n",
    "      return len(self.imgs)\n",
    "\n",
    "    def collect_fn(self, batch):\n",
    "      imgs, labels = zip(*batch)\n",
    "      if self.mode == 'train':\n",
    "        if self.batch_count > 0 and self.batch_count % 10 == 0:\n",
    "          self.width = random.choice(range(224, 256, 16))\n",
    "\n",
    "      self.batch_count += 1\n",
    "      return t.stack(imgs).float(), t.stack(labels)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84
    },
    "colab_type": "code",
    "id": "juc0VDqhLoeQ",
    "outputId": "7214bc88-0842-40a1-8f6b-6b3d73d8a2e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 3, 128, 224]) tensor([[ 2,  6, 10, 10],\n",
      "        [ 1,  2,  6, 10],\n",
      "        [ 1,  4,  1,  5],\n",
      "        [ 6,  2, 10, 10]])\n"
     ]
    }
   ],
   "source": [
    "# test dataset\n",
    "dataset = DigitsDataset(mode='train')\n",
    "loader = DataLoader(dataset, batch_size=4, collate_fn=dataset.collect_fn)\n",
    "img, label = iter(loader).next()\n",
    "# print(len(dataset))\n",
    "# img, label = dataset[0]\n",
    "\n",
    "print(img.shape, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 649
    },
    "colab_type": "code",
    "id": "FxlBdmnhMu_H",
    "outputId": "d8fcd571-43bc-4f15-d185-8717fcd6448e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 12), nrows=4, ncols=2)\n",
    "for i in range(8):\n",
    "  img, label = dataset[i]\n",
    "  img = img * t.tensor([0.229, 0.224, 0.225]).view(3, 1, 1) + t.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)\n",
    "  ax[i//2, i%2].imshow(img.permute(1, 2, 0).numpy())\n",
    "\n",
    "  ax[i//2, i%2].set_xticks([])\n",
    "  ax[i//2, i%2].set_yticks([])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8Gs5nXHr_b6p"
   },
   "source": [
    "## 构建分类模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1RAr_wQx_eh5"
   },
   "outputs": [],
   "source": [
    "class DigitsMobilenet(nn.Module):\n",
    "\n",
    "  def __init__(self, class_num=11):\n",
    "    super(DigitsMobilenet, self).__init__()\n",
    "\n",
    "    self.net = mobilenet_v2(pretrained=True).features\n",
    "    # self.net.classifier = nn.Identity()\n",
    "    self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
    "    self.bn = nn.BatchNorm1d(1280)\n",
    "    self.fc1 = nn.Linear(1280, class_num)\n",
    "    self.fc2 = nn.Linear(1280, class_num)\n",
    "    self.fc3 = nn.Linear(1280, class_num)\n",
    "    self.fc4 = nn.Linear(1280, class_num)\n",
    "    # self.fc5 = nn.Linear(1280, class_num)\n",
    "\n",
    "  def forward(self, img):\n",
    "    features = self.avgpool(self.net(img)).view(-1, 1280)\n",
    "    features = self.bn(features)\n",
    "\n",
    "    fc1 = self.fc1(features)\n",
    "    fc2 = self.fc2(features)\n",
    "    fc3 = self.fc3(features)\n",
    "    fc4 = self.fc4(features)\n",
    "    # fc5 = self.fc5(features)\n",
    "\n",
    "    return fc1, fc2, fc3, fc4\n",
    "\n",
    "\n",
    "class DigitsResnet18(nn.Module):\n",
    "\n",
    "  def __init__(self, class_num=11):\n",
    "    super(DigitsResnet18, self).__init__()\n",
    "    self.net = resnet18(pretrained=True)\n",
    "    self.net.fc = nn.Identity()\n",
    "    # self.net = nn.Sequential(\n",
    "    #     MobileNetV2(num_classes=class_num).features,\n",
    "    #     nn.AdaptiveAvgPool2d((1, 1))\n",
    "    # )\n",
    "    self.bn = nn.BatchNorm1d(512)\n",
    "    self.fc1 = nn.Linear(512, class_num)\n",
    "    self.fc2 = nn.Linear(512, class_num)\n",
    "    self.fc3 = nn.Linear(512, class_num)\n",
    "    self.fc4 = nn.Linear(512, class_num)\n",
    "    # self.fc5 = nn.Linear(512, class_num)\n",
    "\n",
    "  def forward(self, img):\n",
    "    features = self.net(img).squeeze()\n",
    "\n",
    "    fc1 = self.fc1(features)\n",
    "    fc2 = self.fc2(features)\n",
    "    fc3 = self.fc3(features)\n",
    "    fc4 = self.fc4(features)\n",
    "    # fc5 = self.fc5(features)\n",
    "\n",
    "    return fc1, fc2, fc3, fc4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ua1rt4S5QqS1"
   },
   "outputs": [],
   "source": [
    "# 查看网络结构\n",
    "net = resnet18(pretrained=True)\n",
    "net.to(t.device('cuda'))\n",
    "summary(net, input_size=(3, 64, 128), batch_size=config.batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q0_k9GnqPovK"
   },
   "outputs": [],
   "source": [
    "net = DigitsMobilenet()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iJ5VJIB4SeUj"
   },
   "outputs": [],
   "source": [
    "# 查看网络结构\n",
    "net.to(t.device('cuda'))\n",
    "summary(net, input_size=(3, 64, 128), batch_size=config.batch_size)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Lk5WapGI_e8F"
   },
   "source": [
    "## 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QPfxsksDLIub"
   },
   "outputs": [],
   "source": [
    "# ----------------------------------- LabelSmoothEntropy ----------------------------------- #\n",
    "class LabelSmoothEntropy(nn.Module):\n",
    "  def __init__(self, smooth=0.1, class_weights=None, size_average='mean'):\n",
    "    super(LabelSmoothEntropy, self).__init__()\n",
    "    self.size_average = size_average\n",
    "    self.smooth = smooth\n",
    "\n",
    "    self.class_weights = class_weights\n",
    "    \n",
    "\n",
    "  def forward(self, preds, targets):\n",
    "\n",
    "    lb_pos, lb_neg = 1 - self.smooth, self.smooth / (preds.shape[0] - 1)\n",
    "\n",
    "    smoothed_lb = t.zeros_like(preds).fill_(lb_neg).scatter_(1, targets[:, None], lb_pos)\n",
    "\n",
    "    log_soft = F.log_softmax(preds, dim=1)\n",
    "\n",
    "    if self.class_weights is not None:\n",
    "      loss = -log_soft * smoothed_lb * self.class_weights[None, :]\n",
    "\n",
    "    else:\n",
    "      loss = -log_soft * smoothed_lb\n",
    "\n",
    "    loss = loss.sum(1)\n",
    "    if self.size_average == 'mean':\n",
    "      return loss.mean()\n",
    "\n",
    "    elif self.size_average == 'sum':\n",
    "      return loss.sum()\n",
    "    else:\n",
    "      raise NotImplementedError\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eJQmxS86ZRy-"
   },
   "outputs": [],
   "source": [
    "# ----------------------------------- SnapShot ----------------------------------- #\n",
    "class SnapShotBuilder():\n",
    "\n",
    "  \"\"\"\n",
    "  \n",
    "  Params:\n",
    "    n_epoch(integer): total training epoches\n",
    "\n",
    "    n_snap(integer): parameters of model saved times\n",
    "\n",
    "  \"\"\"\n",
    "  def __init__(self, n_epoch, n_snap):\n",
    "\n",
    "    self.n_epoch = n_epoch\n",
    "\n",
    "    self.n_snap = n_snap\n",
    "\n",
    "    pass\n",
    "\n",
    "  def __call__(self):\n",
    "\n",
    "    pass\n",
    "\n",
    "  def _scheduler(self):\n",
    "\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fJ9gBN9v_ty8"
   },
   "outputs": [],
   "source": [
    "class Trainer:\n",
    "\n",
    "  def __init__(self, val=True):\n",
    "\n",
    "    self.device = t.device('cuda') if t.cuda.is_available() else t.device('cpu')\n",
    "\n",
    "    self.train_set = DigitsDataset(mode='train')\n",
    "    self.train_loader = DataLoader(self.train_set, batch_size=config.batch_size, shuffle=True, num_workers=8, pin_memory=True, \\\n",
    "                    drop_last=True, collate_fn=self.train_set.collect_fn)\n",
    "    \n",
    "    if val:\n",
    "      self.val_loader = DataLoader(DigitsDataset(mode='val', aug=False), batch_size=config.batch_size,\\\n",
    "                    num_workers=8, pin_memory=True, drop_last=False)\n",
    "    else:\n",
    "      self.val_loader = None\n",
    "\n",
    "    self.model = DigitsMobilenet(config.class_num).to(self.device)\n",
    "    # self.model = DigitsResnet18(config.class_num).to(self.device)\n",
    "\n",
    "\n",
    "    self.criterion = LabelSmoothEntropy().to(self.device)\n",
    "\n",
    "    # self.optimizer = SGD(self.model.parameters(), lr=config.lr, momentum=config.momentum, weight_decay=config.weights_decay, nesterov=True)\n",
    "    from torch.optim import Adam\n",
    "    self.optimizer = Adam(self.model.parameters(), lr=1e-2)\n",
    "    self.lr_scheduler = CosineAnnealingWarmRestarts(self.optimizer, 10, 2, eta_min=10e-4)\n",
    "    # self.lr_scheduler = (self.optimizer, [10, 20, 30], 0.5)\n",
    "    self.best_acc = 0\n",
    "\n",
    "    if config.pretrained is not None:\n",
    "      self.load_model(config.pretrained)\n",
    "      # print('Load model from %s'%config.pretrained)\n",
    "      if self.val_loader is not None:\n",
    "        acc = self.eval()\n",
    "      self.best_acc = acc\n",
    "      print('Load model from %s, Eval Acc: %.2f'%(config.pretrained, acc * 100))\n",
    "\n",
    "\n",
    "\n",
    "  def train(self):\n",
    "    for epoch in range(config.start_epoch, config.epoches):\n",
    "      acc = self.train_epoch(epoch)\n",
    "      if (epoch + 1) % config.eval_interval == 0:\n",
    "        print('Start Evaluation')\n",
    "        if self.val_loader is not None:\n",
    "          acc = self.eval() \n",
    "\n",
    "        if acc > self.best_acc:\n",
    "          os.makedirs(config.checkpoints, exist_ok=True)\n",
    "          save_path = config.checkpoints+'epoch-resnet18-%d-bn-acc-%.2f.pth'%(epoch+1, acc*100)\n",
    "          self.save_model(save_path)\n",
    "          print('%s saved successfully...'%save_path)\n",
    "          self.best_acc = acc\n",
    "\n",
    "  def train_epoch(self, epoch):\n",
    "    total_loss = 0\n",
    "    corrects = 0\n",
    "    tbar = tqdm(self.train_loader)\n",
    "    self.model.train()\n",
    "    for i, (img, label) in enumerate(tbar):\n",
    "      # print(img.shape)\n",
    "      img = img.to(self.device)\n",
    "      label = label.to(self.device)\n",
    "      self.optimizer.zero_grad()\n",
    "      pred = self.model(img)\n",
    "      loss = self.criterion(pred[0], label[:, 0]) + \\\n",
    "          self.criterion(pred[1], label[:, 1]) + \\\n",
    "          self.criterion(pred[2], label[:, 2]) + \\\n",
    "          self.criterion(pred[3], label[:, 3]) \\\n",
    "          #+ self.criterion(pred[4], label[:, 4])\n",
    "      total_loss += loss.item()\n",
    "      loss.backward() \n",
    "      self.optimizer.step()\n",
    "      temp = t.stack([\\\n",
    "            pred[0].argmax(1) == label[:, 0],\\\n",
    "            pred[1].argmax(1) == label[:, 1],\\\n",
    "            pred[2].argmax(1) == label[:, 2],\\\n",
    "            pred[3].argmax(1) == label[:, 3],], dim=1)\n",
    "\n",
    "      corrects += t.all(temp, dim=1).sum().item()\n",
    "      tbar.set_description('loss: %.3f, acc: %.3f'%(loss/(i+1), corrects*100/((i + 1) * config.batch_size)))\n",
    "      if (i + 1) % config.print_interval == 0:\n",
    "        self.lr_scheduler.step()\n",
    "        \n",
    "    return corrects*100/((i + 1) * config.batch_size)\n",
    "\n",
    "  def eval(self):\n",
    "    self.model.eval()\n",
    "    corrects = 0\n",
    "    with t.no_grad():\n",
    "      tbar = tqdm(self.val_loader)\n",
    "      for i, (img, label) in enumerate(tbar):\n",
    "        img = img.to(self.device)\n",
    "        label = label.to(self.device)\n",
    "        pred = self.model(img)\n",
    "        # temp = t.stack([])\n",
    "        temp = t.stack([\n",
    "\t\t\t      pred[0].argmax(1) == label[:, 0], \\\n",
    "            pred[1].argmax(1) == label[:, 1], \\\n",
    "            pred[2].argmax(1) == label[:, 2], \\\n",
    "            pred[3].argmax(1) == label[:, 3], \\\n",
    "        ], dim=1)\n",
    "\n",
    "        corrects += t.all(temp, dim=1).sum().item()\n",
    "        tbar.set_description('Val Acc: %.2f'%(corrects * 100 /((i+1)*config.batch_size)))\n",
    "    self.model.train()\n",
    "    return corrects / (len(self.val_loader) * config.batch_size)\n",
    "\n",
    "  def save_model(self, save_path, save_opt=False, save_config=False):\n",
    "    dicts = {}\n",
    "    dicts['model'] = self.model.state_dict()\n",
    "    if save_opt:\n",
    "      dicts['opt'] = self.optimizer.state_dict()\n",
    "\n",
    "    if save_config:\n",
    "      dicts['config'] = {s: config.__getattribute__(s) for s in dir(config) if not s.startswith('_')}\n",
    "\n",
    "    t.save(dicts, save_path)\n",
    "\n",
    "  def load_model(self, load_path, changed=False, save_opt=False, save_config=False):\n",
    "\n",
    "    dicts = t.load(load_path)\n",
    "    if not changed:\n",
    "      self.model.load_state_dict(dicts['model'])\n",
    "\n",
    "    else:\n",
    "      dicts = t.load(load_path)['model']\n",
    "\n",
    "      keys = list(net.state_dict().keys())\n",
    "      values = list(dicts.values())\n",
    "\n",
    "      new_dicts = {k: v for k, v in zip(keys, values)}\n",
    "      self.model.load_state_dict(new_dicts)\n",
    "\n",
    "    if save_opt:\n",
    "      self.optimizer.load_state_dict(dicts['opt'])\n",
    "\n",
    "    if save_config:\n",
    "      for k, v in dicts['config'].items():\n",
    "        config.__setattr__(k, v)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HaDkWOHmsjgP"
   },
   "outputs": [],
   "source": [
    "trainer = Trainer()\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7ho7EdKeoqR8"
   },
   "source": [
    "## 多模型堆叠验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 82,
     "referenced_widgets": [
      "1e466f74ee6440cb9f0b7cfcac2be653",
      "72c00adf5333478c911989fac90f94dc",
      "81a393c6e5e0477ca14a4a0888cb6bf5",
      "50e0338a6d604711a220f13ea1301cc5",
      "9840733f2a444ef59d230bb1e0e9a462",
      "d664219dcf544c77bfbb0178a754c3e4",
      "9d5c658f9e054fca8abcc7427ae0f014",
      "26456224c27e4ef8929b6ef4500c4e42"
     ]
    },
    "colab_type": "code",
    "id": "GBklWPJ75u45",
    "outputId": "08713a93-4a97-4a93-b0e6-3f8de6bf51cb"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e466f74ee6440cb9f0b7cfcac2be653",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=157.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Results.saved to /content/drive/My Drive/Data/Datawhale-DigitsRecognition/results-mb.csv\n"
     ]
    }
   ],
   "source": [
    "def stack_eval(mb_path, res_path):\n",
    "  mb_net = DigitsMobilenet().cuda()\n",
    "  dicts = t.load(mb_path)['model']\n",
    "  # dicts.popitem()\n",
    "  # dicts.popitem()\n",
    "  mb_net.load_state_dict(dicts)\n",
    "\n",
    "\n",
    "  res_net = DigitsResnet18().cuda()\n",
    "  res_net.load_state_dict(t.load(res_path)['model'])\n",
    "  \n",
    "  res_net.eval()\n",
    "  mb_net.eval()\n",
    "  dataset = DigitsDataset(mode='val', aug=False)\n",
    "  imgs = glob(data_dir['val_data']+'*.png')\n",
    "  labels = json.load(open(data_dir['val_label'], 'r'))\n",
    "  dataset.imgs = [(img, labels[os.path.split(img)[-1]]) for img in imgs if os.path.split(img)[-1] in labels]\n",
    "  val_loader = DataLoader(dataset, batch_size=config.batch_size, shuffle=False,\\\n",
    "                    num_workers=16, pin_memory=True, drop_last=False)\n",
    "  corrects = 0\n",
    "  with t.no_grad():\n",
    "    tbar = tqdm(val_loader)\n",
    "    for i, (img, label) in enumerate(tbar):\n",
    "      img = img.cuda()\n",
    "      label = t.tensor(label).long().cuda()\n",
    "      pred = [0.4 * a + 0.6 * b for a, b in zip(res_net(img), mb_net(img))]\n",
    "\n",
    "\n",
    "      # temp = t.stack([])\n",
    "      temp = t.stack([\n",
    "\t\t      pred[0].argmax(1) == label[:, 0], \\\n",
    "          pred[1].argmax(1) == label[:, 1], \\\n",
    "          pred[2].argmax(1) == label[:, 2], \\\n",
    "          pred[3].argmax(1) == label[:, 3], \\\n",
    "      ], dim=1)\n",
    "      corrects += t.all(temp, dim=1).sum().item()\n",
    "      tbar.set_description('Val Acc: %.2f'%(corrects * 100 /((i+1)*config.batch_size)))\n",
    "  self.model.train()\n",
    "  return corrects / (len(val_loader) * config.batch_size)\n",
    "  \n",
    "def stack_predict(*model, batch_size=64):\n",
    "  val_loader = DataLoader(DigitsDataset(mode='test', aug=False), batch_size=batch_size, shuffle=False,\\\n",
    "                      num_workers=8, pin_memory=True, drop_last=False)\n",
    "  \n",
    "  \n",
    "  results = []\n",
    "  corrects = 0\n",
    "  total = 0\n",
    "  for m in model:\n",
    "    m.eval()\n",
    "    m.cuda()\n",
    "  tbar = tqdm(val_loader)\n",
    "  with t.no_grad():\n",
    "    for i, (img, label) in enumerate(tbar):\n",
    "      img = img.cuda()\n",
    "      # label = label.cuda()\n",
    "      # ---------取平均值----------#\n",
    "      # ------------74.51---------------#\n",
    "      pred = [0]*4\n",
    "      for a in [m(img) for m in model]:\n",
    "\n",
    "        pred = [p + a_ for p, a_ in zip(pred, a)]\n",
    "      pred = [p / len(model) for p in pred]\n",
    "      results += [[name, code] for name, code in zip(label, parse2class(pred))]\n",
    "\n",
    "  results = sorted(results, key=lambda x: x[0])\n",
    "\n",
    "  write2csv(results)\n",
    "\n",
    "model_paths = glob('/content/drive/My Drive/Data/Datawhale-DigitsRecognition/checkpoints/*.pth')\n",
    "models = []\n",
    "for m in model_paths:\n",
    "  if 'resnet' in m:\n",
    "    model = DigitsResnet18()\n",
    "  elif 'mobilenet' in m:\n",
    "    model = DigitsMobilenet()\n",
    "  model.load_state_dict(t.load(m)['model'])\n",
    "  models.append(model)\n",
    "        \n",
    "stack_predict(*models, batch_size=256)\n",
    "# mb_path = '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/checkpoints/epoch-50-mobilenet-bn-acc-0.9284.pth'\n",
    "# res_path = '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/checkpoints/epoch-34-bn-_acc-71.30.pth'\n",
    "# print(stack_eval(mb_path, res_path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mt6tq9H4_wuv"
   },
   "source": [
    "## 预测并生成结果文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PD2ISKAC_66H"
   },
   "outputs": [],
   "source": [
    "def predicts(model_path):\n",
    "  test_loader = DataLoader(DigitsDataset(mode='test', aug=False), batch_size=config.batch_size, shuffle=False,\\\n",
    "                    num_workers=8, pin_memory=True, drop_last=False)\n",
    "  results = []\n",
    "  \n",
    "  mb_path = '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/checkpoints/epoch-50-mobilenet-bn-acc-0.9284.pth'\n",
    "  # res_path = '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/checkpoints/epoch-34-resnet18-bn-acc-71.30.pth'\n",
    "  mb_net = DigitsMobilenet().cuda()\n",
    "  # dicts = t.load(mb_path)['model']\n",
    "  # dicts.popitem()\n",
    "  # dicts.popitem()\n",
    "  mb_net.load_state_dict(t.load(mb_path)['model'])\n",
    "\n",
    "\n",
    "  res_net = DigitsResnet18().cuda()\n",
    "  res_net.load_state_dict(t.load(res_path)['model'])\n",
    "  # print('Load model from %s successfully'%model_path)\n",
    "\n",
    "  tbar = tqdm(test_loader)\n",
    "  mb_net.eval()\n",
    "  res_net.eval()\n",
    "  with t.no_grad():\n",
    "    for i, (img, img_names) in enumerate(tbar):\n",
    "      img = img.cuda()\n",
    "      pred = mb_net(img)\n",
    "      # pred = [0.4 * a + 0.6 * b for a, b in zip(res_net(img), mb_net(img))]\n",
    "      results += [[name, code] for name, code in zip(img_names, parse2class(pred))]\n",
    "\n",
    "  # result.sort(key=results)\n",
    "  results = sorted(results, key=lambda x: x[0])\n",
    "\n",
    "  write2csv(results)\n",
    "  return results\n",
    "\n",
    "\n",
    "\n",
    "def parse2class(prediction):\n",
    "  \"\"\"\n",
    "  \n",
    "  Params:\n",
    "    prediction(tuple of tensor): \n",
    "\n",
    "  \n",
    "  \"\"\"\n",
    "  ch1, ch2, ch3, ch4 = prediction\n",
    "\n",
    "  char_list = [str(i) for i in range(10)]\n",
    "  char_list.append('')\n",
    "\n",
    "\n",
    "  ch1, ch2, ch3, ch4 = ch1.argmax(1), ch2.argmax(1), ch3.argmax(1), ch4.argmax(1)\n",
    "\n",
    "  ch1, ch2, ch3, ch4 = [char_list[i.item()] for i in ch1], [char_list[i.item()] for i in ch2], \\\n",
    "                  [char_list[i.item()] for i in ch3], [char_list[i.item()] for i in ch4] \\\n",
    "                  # ，[char_list[i.item()] for i in ch5]\n",
    "\n",
    "  # res = [c1+c2+c3+c4+c5 for c1, c2, c3, c4, c5 in zip(ch1, ch2, ch3, ch4, ch5)]  \n",
    "  res = [c1+c2+c3+c4 for c1, c2, c3, c4 in zip(ch1, ch2, ch3, ch4)]             \n",
    "  return res\n",
    "\n",
    "\n",
    "def write2csv(results):\n",
    "  \"\"\"\n",
    "  \n",
    "  results(list):\n",
    "\n",
    "  \"\"\"\n",
    "\n",
    "  df = pd.DataFrame(results, columns=['file_name', 'file_code'])\n",
    "  df['file_name'] = df['file_name'].apply(lambda x: x.split('/')[-1])\n",
    "  save_name = '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/results-mb.csv'\n",
    "  df.to_csv(save_name, sep=',', index=None)\n",
    "  print('Results.saved to %s'%save_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "61b1cfe02d1240298e1f8f61e00f5f29",
      "72dda389fc284e5fa0e9b0beb4a172f3",
      "c13021e7eaf44f3ab4a787a6e1d37df1",
      "8477fdaff53740dbb85413d2d0da680f",
      "7c6f307114f8498494c070eea88389a3",
      "7ed775cc37564075bb5871253b99c2c4",
      "67841db76cd74f19aaf7d768dbbb79c7",
      "6cc1adfdcb5545d4a2cce397b8fd1b94"
     ]
    },
    "colab_type": "code",
    "id": "V1ukIqhOOq2d",
    "outputId": "7710bab2-88f1-4ccf-a6cf-0326e9550c2e"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61b1cfe02d1240298e1f8f61e00f5f29",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=625.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Results.saved to /content/drive/My Drive/Data/Datawhale-DigitsRecognition/results-mb.csv\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[['/content/data/mchar_test_a/000000.png', '59'],\n",
       " ['/content/data/mchar_test_a/000001.png', '290'],\n",
       " ['/content/data/mchar_test_a/000002.png', '16'],\n",
       " ['/content/data/mchar_test_a/000003.png', '39'],\n",
       " ['/content/data/mchar_test_a/000004.png', '95'],\n",
       " ['/content/data/mchar_test_a/000005.png', '23'],\n",
       " ['/content/data/mchar_test_a/000006.png', '26'],\n",
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